Computer Science
|
Program code |
Intakes |
Duration |
Mode of study |
Medium of instruction |
|
9480101 |
June December |
3-4 years |
Full-time |
English |
Introduction
The objective of the doctoral program in Computer Science at the Faculty of Information Technology, Ton Duc Thang University, is to train highly qualified experts and scientists who are able to gain access to scientific and up-to-date knowledge in the world. It also intends to develop creativity and solve complex problems in the field particularly of Computer Science and Information Technology in general.
Curriculum apply for Ph.D. candidate with a Master's degree:
|
Course code |
Course title |
Credit |
Theory |
Practice Experiment Discussion |
|
A. Compulsory specialized courses |
3 |
|
|
|
|
IT801100 |
Advanced Machine Learning |
3 |
3 |
0 |
|
B. Elective specialized courses |
9 |
|
|
|
|
IT801020 |
Advanced Topics in Mathematics for Computer Science |
3 |
3 |
0 |
|
IT801030 |
Advanced topics in Natural language processing |
3 |
3 |
0 |
|
IT801040 |
Advanced topics in Data analysis |
3 |
3 |
0 |
|
IT801050 |
Advanced topics in Computer vision |
3 |
3 |
0 |
|
IT801060 |
Advanced topics in Information security |
3 |
3 |
0 |
|
IT801070 |
Advanced topics in Data mining |
3 |
3 |
0 |
|
IT801080 |
Advanced topics in Digital Image Processing |
3 |
3 |
0 |
|
IT801010 |
Advanced topics in Artificial Intelligence |
3 |
3 |
0 |
|
C. Literature review |
4 |
|
|
|
|
IT801900 |
Research proposal |
4 |
4 |
0 |
|
D. Doctoral research topic |
6 |
|
|
|
|
IT801930 |
Research topic 1 |
3 |
3 |
0 |
|
IT801940 |
Research topic 2 |
3 |
3 |
0 |
|
E. Graduation |
|
70 |
|
|
|
IT801000 |
Doctoral Dissertation |
70 |
70 |
0 |
|
Total |
92 |
|
||
Curriculum apply for Ph.D. candidate with a Bachelor's degree:
|
Course code |
Course title |
Credit |
Theory |
Practice Experiment Discussion |
|
A. Fundamental courses |
30 |
|
|
|
|
A1. Compulsory general knowledge |
3 |
|
|
|
|
SH700010 |
Philosophy |
3 |
3 |
0 |
|
A2. Compulsory fundamental knowledge |
3 |
|
|
|
|
IT701010 |
Machine Learning |
3 |
3 |
0 |
|
A3. Elective fundamental knowledge |
24 |
|
|
|
|
A3.1. Elective courses |
15 |
|
|
|
|
IT701400 |
Digital Image Processing |
3 |
3 |
0 |
|
IT701100 |
Probabilistic Graphical Models |
3 |
3 |
0 |
|
IT701040 |
Distributed Systems |
3 |
3 |
0 |
|
IT701050 |
Information Security |
3 |
3 |
0 |
|
IT701080 |
Knowledge - based Systems |
3 |
3 |
0 |
|
IT701110 |
Cryptography |
3 |
3 |
0 |
|
IT701120 |
Computer Vision |
3 |
3 |
0 |
|
IT701150 |
Mining Massive Data Sets |
3 |
3 |
0 |
|
IT701220 |
Data Mining |
3 |
3 |
0 |
|
IT701200 |
Natural Language Processing |
3 |
3 |
0 |
|
IT701210 |
Spoken Language Processing |
3 |
3 |
0 |
|
IT701240 |
Advanced Topics in Data Science |
3 |
3 |
0 |
|
IT701290 |
Cybersecurity |
3 |
3 |
0 |
|
IT701300 |
Topics in Deep learning |
3 |
3 |
0 |
|
IT701320 |
IoT Application Development |
3 |
3 |
0 |
|
A3.2. Elective research topics |
9 |
|
|
|
|
IT701370 |
Research Topic 1 |
3 |
3 |
0 |
|
IT701380 |
Research Topic 2 |
3 |
3 |
0 |
|
IT701390 |
Research Topic 3 |
3 |
3 |
0 |
|
IT701360 |
Research Project |
9 |
9 |
0 |
|
B. Compulsory specialized courses |
3 |
|
|
|
|
IT801010 |
Advanced topics in Artificial Intelligence |
3 |
3 |
0 |
|
C. Elective specialized courses |
9 |
|
|
|
|
IT801020 |
Advanced Topics in Mathematics for Computer Science |
3 |
3 |
0 |
|
IT801030 |
Advanced topics in Natural language processing |
3 |
3 |
0 |
|
IT801040 |
Topics in Data analysis |
3 |
3 |
0 |
|
IT801050 |
Advanced topics in Computer vision |
3 |
3 |
0 |
|
IT801060 |
Advanced topics in Information security |
3 |
3 |
0 |
|
IT801070 |
Advanced topics in Data mining |
3 |
3 |
0 |
|
IT801080 |
Advanced topics in Digital Image Processing |
3 |
3 |
0 |
|
IT801100 |
Advanced Machine Learning |
3 |
3 |
0 |
|
D. Literature review |
4 |
|
|
|
|
IT801900 |
Research proposal |
4 |
4 |
0 |
|
E. Doctoral research topic |
6 |
|
|
|
|
IT801930 |
Research topic 1 |
3 |
3 |
0 |
|
IT801940 |
Research topic 2 |
3 |
3 |
0 |
|
F. Graduation |
70 |
|
|
|
|
IT801000 |
Doctoral Dissertation |
70 |
70 |
0 |
|
Total |
122 |
|
||
Note: 1 credit = 15 theory periods or exercises
= 30 periods of presentation, discussion or practice
